Generative Adversarial Stacked Autoencoders for Facial Pose Normalization and Emotion Recognition

In this work, we propose a novel Generative Adversarial Stacked Autoencoder that learns to map facial expressions with up to ±60 degrees to an illumination invariant facial representation of 0 degrees. We accomplish this by using a novel convolutional layer that exploits both local and global spatial information, and a convolutional layer with a reduced number of parameters that exploits facial symmetry. Furthermore, we introduce a generative adversarial gradual greedy layer-wise learning algorithm designed to train Adversarial Autoencoders in an efficient and incremental manner. We demonstrate the efficiency of our method and report state-of-the-art performance on several facial emotion recognition corpora, including one collected in the wild.